def nodes(self):
"""A |Nodes| collection of all required nodes.
>>> from hydpy import RiverBasinNumbers2Selection
>>> rbns2s = RiverBasinNumbers2Selection(
... (111, 113, 1129, 11269, 1125, 11261,
... 11262, 1123, 1124, 1122, 1121))
Note that the required outlet node is added:
>>> rbns2s.nodes
Nodes("node_1123", "node_1125", "node_11269", "node_1129", "node_113",
"node_outlet")
It is both possible to change the prefix names of the nodes and
the name of the outlet node separately:
>>> rbns2s.node_prefix = 'b_'
>>> rbns2s.last_node = 'l_node'
>>> rbns2s.nodes
Nodes("b_1123", "b_1125", "b_11269", "b_1129", "b_113", "l_node")
"""
return (devicetools.Nodes(self.node_prefix + routers
for routers in self._router_numbers) +
devicetools.Node(self.last_node))
def __call__(self, *args, **kwargs) -> None:
nodes = []
coordinates = numpy.empty((len(kwargs), 2), dtype=float)
for idx, (name, values) in enumerate(kwargs.items()):
nodes.append(devicetools.Node(name))
coordinates[idx, :] = values
self.nodes = tuple(nodes)
self.__hydpy__set_shape__((len(nodes), 2))
self.__hydpy__set_value__(coordinates)
def __call__(self, *args, **kwargs) -> None:
nodes = []
heights = numpy.empty(len(kwargs), dtype=float)
for idx, (name, value) in enumerate(kwargs.items()):
nodes.append(devicetools.Node(name))
heights[idx] = value
self.nodes = tuple(nodes)
self.__hydpy__set_shape__(len(nodes))
self.__hydpy__set_value__(heights)
def nodes(self) -> devicetools.Nodes:
"""A |Nodes| object containing all required nodes, including the
outlet node.
>>> from hydpy import RiverBasinNumbers2Selection
>>> rbns2s = RiverBasinNumbers2Selection(
... (111, 113, 1129, 11269, 1125, 11261,
... 11262, 1123, 1124, 1122, 1121))
>>> rbns2s.nodes
Nodes("node_1123", "node_1125", "node_11269", "node_1129", "node_113",
"node_outlet")
It is both possible to change the prefix names of the nodes and
the name of the outlet node separately:
>>> rbns2s.node_prefix = "b_"
>>> rbns2s.last_node = "l_node"
>>> rbns2s.nodes
Nodes("b_1123", "b_1125", "b_11269", "b_1129", "b_113", "l_node")
"""
return devicetools.Nodes(
self.node_prefix + routers
for routers in self._router_numbers) + devicetools.Node(
self.last_node)
def prepare_io_example_1() -> Tuple[devicetools.Nodes, devicetools.Elements]:
"""Prepare an IO example configuration for testing purposes.
Function |prepare_io_example_1| is thought for testing the functioning
of *HydPy* and thus should be of interest for framework developers only.
It uses the application models |lland_v1| and |lland_v2|. Here, we
apply |prepare_io_example_1| and shortly discuss different aspects of
the data it generates.
>>> from hydpy.examples import prepare_io_example_1
>>> nodes, elements = prepare_io_example_1()
(1) It defines a short initialisation period of five days:
>>> from hydpy import pub
>>> pub.timegrids
Timegrids("2000-01-01 00:00:00",
"2000-01-05 00:00:00",
"1d")
(2) It creates a flat IO testing directory structure:
>>> pub.sequencemanager.inputdirpath
'inputpath'
>>> pub.sequencemanager.fluxdirpath
'outputpath'
>>> pub.sequencemanager.statedirpath
'outputpath'
>>> pub.sequencemanager.nodedirpath
'nodepath'
>>> import os
>>> from hydpy import TestIO
>>> with TestIO():
... print(sorted(filename for filename in os.listdir(".")
... if not filename.startswith("_")))
['inputpath', 'nodepath', 'outputpath']
(3) It returns three |Element| objects handling either application model
|lland_v1| or |lland_v2|, and two |Node| objects handling variables
`Q` and `T`:
>>> for element in elements:
... print(element.name, element.model)
element1 lland_v1
element2 lland_v1
element3 lland_v2
>>> for node in nodes:
... print(node.name, node.variable)
node1 Q
node2 T
(4) It generates artificial time series data of the input sequence
|lland_inputs.Nied|, the flux sequence |lland_fluxes.NKor|, and the
state sequence |lland_states.BoWa| of each model instance, and the
|Sim| sequence of each node instance. For unambiguous test results,
all generated values are unique:
>>> nied1 = elements.element1.model.sequences.inputs.nied
>>> nied1.series
InfoArray([ 0., 1., 2., 3.])
>>> nkor1 = elements.element1.model.sequences.fluxes.nkor
>>> nkor1.series
InfoArray([[ 12.],
[ 13.],
[ 14.],
[ 15.]])
>>> bowa3 = elements.element3.model.sequences.states.bowa
>>> bowa3.series
InfoArray([[ 48., 49., 50.],
[ 51., 52., 53.],
[ 54., 55., 56.],
[ 57., 58., 59.]])
>>> sim2 = nodes.node2.sequences.sim
>>> sim2.series
InfoArray([ 64., 65., 66., 67.])
(5) All sequences carry |numpy.ndarray| objects with (deep) copies
of the time series data for testing:
>>> import numpy
>>> (numpy.all(nied1.series == nied1.testarray) and
... numpy.all(nkor1.series == nkor1.testarray) and
... numpy.all(bowa3.series == bowa3.testarray) and
... numpy.all(sim2.series == sim2.testarray))
InfoArray(True, dtype=bool)
>>> bowa3.series[1, 2] = -999.0
>>> numpy.all(bowa3.series == bowa3.testarray)
InfoArray(False, dtype=bool)
"""
testtools.TestIO.clear()
hydpy.pub.sequencemanager = filetools.SequenceManager()
with testtools.TestIO():
hydpy.pub.sequencemanager.inputdirpath = "inputpath"
hydpy.pub.sequencemanager.fluxdirpath = "outputpath"
hydpy.pub.sequencemanager.statedirpath = "outputpath"
hydpy.pub.sequencemanager.nodedirpath = "nodepath"
hydpy.pub.timegrids = "2000-01-01", "2000-01-05", "1d"
node1 = devicetools.Node("node1")
node2 = devicetools.Node("node2", variable="T")
nodes = devicetools.Nodes(node1, node2)
element1 = devicetools.Element("element1", outlets=node1)
element2 = devicetools.Element("element2", outlets=node1)
element3 = devicetools.Element("element3", outlets=node1)
elements = devicetools.Elements(element1, element2, element3)
element1.model = importtools.prepare_model("lland_v1")
element2.model = importtools.prepare_model("lland_v1")
element3.model = importtools.prepare_model("lland_v2")
for idx, element in enumerate(elements):
parameters = element.model.parameters
parameters.control.nhru(idx + 1)
parameters.control.lnk(lland.ACKER)
parameters.derived.absfhru(10.0)
# pylint: disable=not-callable
# pylint usually understands that all options are callable
# but, for unknown reasons, not in the following line:
with hydpy.pub.options.printprogress(False):
nodes.prepare_simseries()
elements.prepare_inputseries()
elements.prepare_fluxseries()
elements.prepare_stateseries()
# pylint: enable=not-callable
def init_values(seq, value1_):
value2_ = value1_ + len(seq.series.flatten())
values_ = numpy.arange(value1_, value2_, dtype=float)
seq.testarray = values_.reshape(seq.seriesshape)
seq.series = seq.testarray.copy()
return value2_
value1 = 0
for subname, seqname in zip(["inputs", "fluxes", "states"],
["nied", "nkor", "bowa"]):
for element in elements:
subseqs = getattr(element.model.sequences, subname)
value1 = init_values(getattr(subseqs, seqname), value1)
for node in nodes:
value1 = init_values(node.sequences.sim, value1)
return nodes, elements
def prepare_io_example_1() -> Tuple[devicetools.Nodes, devicetools.Elements]:
"""Prepare an IO example configuration for testing purposes.
Function |prepare_io_example_1| is thought for testing the functioning of *HydPy*
and thus should be of interest for framework developers only. It uses the
application models |lland_v1|, |lland_v2|, and |hland_v1|. Here, we apply
|prepare_io_example_1| and shortly discuss different aspects of its generated data.
>>> from hydpy.examples import prepare_io_example_1
>>> nodes, elements = prepare_io_example_1()
(1) It defines a short initialisation period of five days:
>>> from hydpy import pub
>>> pub.timegrids
Timegrids("2000-01-01 00:00:00",
"2000-01-05 00:00:00",
"1d")
(2) It prepares an empty directory for IO testing:
>>> import os
>>> from hydpy import repr_, TestIO
>>> with TestIO(): # doctest: +ELLIPSIS
... repr_(pub.sequencemanager.currentpath)
... os.listdir("project/series/default")
'...iotesting/project/series/default'
[]
(3) It returns four |Element| objects handling either application model |lland_v1|
|lland_v2|, or |hland_v1|, and two |Node| objects handling variables `Q` and `T`:
>>> for element in elements:
... print(element.name, element.model)
element1 lland_v1
element2 lland_v1
element3 lland_v2
element4 hland_v1
>>> for node in nodes:
... print(node.name, node.variable)
node1 Q
node2 T
(4) It generates artificial time series data for the input sequence
|lland_inputs.Nied|, the flux sequence |lland_fluxes.NKor|, and the state sequence
|lland_states.BoWa| of each |lland| model instance, the state sequence
|hland_states.SP| of the |hland_v1| model instance, and the |Sim| sequence of each
node instance. For precise test results, all generated values are unique:
>>> nied1 = elements.element1.model.sequences.inputs.nied
>>> nied1.series
InfoArray([0., 1., 2., 3.])
>>> nkor1 = elements.element1.model.sequences.fluxes.nkor
>>> nkor1.series
InfoArray([[12.],
[13.],
[14.],
[15.]])
>>> bowa3 = elements.element3.model.sequences.states.bowa
>>> bowa3.series
InfoArray([[48., 49., 50.],
[51., 52., 53.],
[54., 55., 56.],
[57., 58., 59.]])
>>> sim2 = nodes.node2.sequences.sim
>>> sim2.series
InfoArray([64., 65., 66., 67.])
>>> sp4 = elements.element4.model.sequences.states.sp
>>> sp4.series
InfoArray([[[68., 69., 70.],
[71., 72., 73.]],
<BLANKLINE>
[[74., 75., 76.],
[77., 78., 79.]],
<BLANKLINE>
[[80., 81., 82.],
[83., 84., 85.]],
<BLANKLINE>
[[86., 87., 88.],
[89., 90., 91.]]])
(5) All sequences carry |numpy.ndarray| objects with (deep) copies of the
time-series data for testing:
>>> import numpy
>>> (numpy.all(nied1.series == nied1.testarray) and
... numpy.all(nkor1.series == nkor1.testarray) and
... numpy.all(bowa3.series == bowa3.testarray) and
... numpy.all(sim2.series == sim2.testarray) and
... numpy.all(sp4.series == sp4.testarray))
InfoArray(True)
>>> bowa3.series[1, 2] = -999.0
>>> numpy.all(bowa3.series == bowa3.testarray)
InfoArray(False)
"""
testtools.TestIO.clear()
hydpy.pub.projectname = "project"
hydpy.pub.sequencemanager = filetools.SequenceManager()
with testtools.TestIO():
os.makedirs("project/series/default")
hydpy.pub.timegrids = "2000-01-01", "2000-01-05", "1d"
node1 = devicetools.Node("node1")
node2 = devicetools.Node("node2", variable="T")
nodes = devicetools.Nodes(node1, node2)
element1 = devicetools.Element("element1", outlets=node1)
element2 = devicetools.Element("element2", outlets=node1)
element3 = devicetools.Element("element3", outlets=node1)
element4 = devicetools.Element("element4", outlets=node1)
elements_lland = devicetools.Elements(element1, element2, element3)
elements = elements_lland + element4
element1.model = importtools.prepare_model("lland_v1")
element2.model = importtools.prepare_model("lland_v1")
element3.model = importtools.prepare_model("lland_v2")
element4.model = importtools.prepare_model("hland_v1")
for idx, element in enumerate(elements_lland):
parameters = element.model.parameters
parameters.control.nhru(idx + 1)
parameters.control.lnk(lland.ACKER)
parameters.derived.absfhru(10.0)
control = element4.model.parameters.control
control.nmbzones(3)
control.sclass(2)
control.zonetype(hland.FIELD)
control.zonearea.values = 10.0
# pylint: disable=not-callable
# pylint usually understands that all options are callable
# but, for unknown reasons, not in the following line:
with hydpy.pub.options.printprogress(False):
nodes.prepare_simseries(allocate_ram=False) # ToDo: add option "reset"
nodes.prepare_simseries(allocate_ram=True)
elements.prepare_inputseries(allocate_ram=False)
elements.prepare_inputseries(allocate_ram=True)
elements.prepare_factorseries(allocate_ram=False)
elements.prepare_factorseries(allocate_ram=True)
elements.prepare_fluxseries(allocate_ram=False)
elements.prepare_fluxseries(allocate_ram=True)
elements.prepare_stateseries(allocate_ram=False)
elements.prepare_stateseries(allocate_ram=True)
# pylint: enable=not-callable
def init_values(seq: TestIOSequence, value1_: float) -> float:
value2_ = value1_ + len(seq.series.flatten())
values_ = numpy.arange(value1_, value2_, dtype=float)
seq.testarray = values_.reshape(seq.seriesshape)
seq.series = seq.testarray.copy()
return value2_
value1 = 0.0
for subname, seqname in zip(["inputs", "fluxes", "states"],
["nied", "nkor", "bowa"]):
for element in elements_lland:
subseqs = getattr(element.model.sequences, subname)
value1 = init_values(getattr(subseqs, seqname), value1)
for node in nodes:
value1 = init_values(node.sequences.sim,
value1) # type: ignore[arg-type]
init_values(element4.model.sequences.states.sp,
value1) # type: ignore[arg-type]
return nodes, elements